Hierarchical multiple regression (not to be confused with hierarchical linear models) is similar to stepwise regression, but the researcher, not the computer, determines the order of entry of the variables. F-tests are used to compute the significance of each added variable (or set of variables) to the explanation reflected in R-square. This hierarchical procedure is an alternative to comparing betas for purposes of assessing the importance of the independents. In more complex forms of hierarchical regression, the model may involve a series of intermediate variables which are dependents with respect to some other independents, but are themselves independents with respect to the ultimate dependent. Hierarchical multiple regression may then involve a series of regressions for each intermediate as well as for the ultimate dependent.
- For hierarchical multiple regression, in SPSS first specify the dependent variable; then enter the first independent variable or set of variables in the independent variables box; click on "Next" to clear the IV box and enter a second variable or set of variables; etc. One also clicks on the Statistics button and selects "R-squared change." Note that the error term will change for each block or step in the hierarchical analysis. If this is not desired, it can be avoided by selecting Statistics, General Linear Model, GLM-General Factorial, then specifying Type I sums of squares. This will yield GLM results analogous to hierarchical regression but with the same error term across blocks.